topostats.unet_masking
======================

.. py:module:: topostats.unet_masking

.. autoapi-nested-parse::

   Segment grains using a U-Net model.

   ..
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Attributes
----------

.. autoapisummary::

   topostats.unet_masking.LOGGER


Functions
---------

.. autoapisummary::

   topostats.unet_masking.dice_loss
   topostats.unet_masking.iou_loss
   topostats.unet_masking.mean_iou
   topostats.unet_masking.predict_unet
   topostats.unet_masking.make_bounding_box_square
   topostats.unet_masking.pad_bounding_box


Module Contents
---------------

.. py:data:: LOGGER

.. py:function:: dice_loss(y_true: numpy.typing.NDArray[numpy.float32], y_pred: numpy.typing.NDArray[numpy.float32], smooth: float = 1e-05) -> tensorflow.Tensor

   
   DICE loss function.

   Expects y_true and y_pred to be of shape (batch_size, height, width, 1).

   :param y_true: True values.
   :type y_true: npt.NDArray[np.float32]
   :param y_pred: Predicted values.
   :type y_pred: npt.NDArray[np.float32]
   :param smooth: Smoothing factor to prevent division by zero.
   :type smooth: float

   :returns: The DICE loss.
   :rtype: tf.Tensor















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.. py:function:: iou_loss(y_true: numpy.typing.NDArray[numpy.float32], y_pred: numpy.typing.NDArray[numpy.float32], smooth: float = 1e-05) -> tensorflow.Tensor

   
   Intersection over Union loss function.

   Expects y_true and y_pred to be of shape (batch_size, height, width, 1).

   :param y_true: True values.
   :type y_true: npt.NDArray[np.float32]
   :param y_pred: Predicted values.
   :type y_pred: npt.NDArray[np.float32]
   :param smooth: Smoothing factor to prevent division by zero.
   :type smooth: float

   :returns: The IoU loss.
   :rtype: tf.Tensor















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.. py:function:: mean_iou(y_true: numpy.typing.NDArray[numpy.float32], y_pred: numpy.typing.NDArray[numpy.float32])

   
   Mean Intersection Over Union metric, ignoring the background class.

   :param y_true: True values.
   :type y_true: npt.NDArray[np.float32]
   :param y_pred: Predicted values.
   :type y_pred: npt.NDArray[np.float32]

   :returns: The mean IoU.
   :rtype: tf.Tensor















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.. py:function:: predict_unet(image: numpy.typing.NDArray[numpy.float32], model: keras.Model, confidence: float, model_input_shape: tuple[int | None, int, int, int], upper_norm_bound: float, lower_norm_bound: float) -> numpy.typing.NDArray[numpy.bool_]

   
   Predict cats segmentation from a flattened image.

   :param image: The image to predict the mask for.
   :type image: npt.NDArray[np.float32]
   :param model: The U-Net model.
   :type model: keras.Model
   :param confidence: The confidence threshold for the mask.
   :type confidence: float
   :param model_input_shape: The shape of the model input, including the batch and channel dimensions.
   :type model_input_shape: tuple[int | None, int, int, int]
   :param upper_norm_bound: The upper bound for normalising the image.
   :type upper_norm_bound: float
   :param lower_norm_bound: The lower bound for normalising the image.
   :type lower_norm_bound: float

   :returns: The predicted mask.
   :rtype: npt.NDArray[np.bool_]















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.. py:function:: make_bounding_box_square(crop_min_row: int, crop_min_col: int, crop_max_row: int, crop_max_col: int, image_shape: tuple[int, int]) -> tuple[int, int, int, int]

   
   Make a bounding box square.

   :param crop_min_row: The minimum row index of the crop.
   :type crop_min_row: int
   :param crop_min_col: The minimum column index of the crop.
   :type crop_min_col: int
   :param crop_max_row: The maximum row index of the crop.
   :type crop_max_row: int
   :param crop_max_col: The maximum column index of the crop.
   :type crop_max_col: int
   :param image_shape: The shape of the image.
   :type image_shape: tuple[int, int]

   :returns: The new crop indices.
   :rtype: tuple[int, int, int, int]















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.. py:function:: pad_bounding_box(crop_min_row: int, crop_min_col: int, crop_max_row: int, crop_max_col: int, image_shape: tuple[int, int], padding: int) -> tuple[int, int, int, int]

   
   Pad a bounding box.

   :param crop_min_row: The minimum row index of the crop.
   :type crop_min_row: int
   :param crop_min_col: The minimum column index of the crop.
   :type crop_min_col: int
   :param crop_max_row: The maximum row index of the crop.
   :type crop_max_row: int
   :param crop_max_col: The maximum column index of the crop.
   :type crop_max_col: int
   :param image_shape: The shape of the image.
   :type image_shape: tuple[int, int]
   :param padding: The padding to apply to the bounding box.
   :type padding: int

   :returns: The new crop indices.
   :rtype: tuple[int, int, int, int]















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